Speech recognition systems generally use speech recognition models to recognize speech. For automatic dictation or speech-to-text processing applications, the recognized speech can then be converted into corresponding text. Alternatively or in addition, for natural language processing (NLP) applications, the recognized speech can be interpreted and action can be taken based thereon.
A speech recognition model generally includes an acoustic model and a language model. Acoustic models generally model the relationships between audio signals (e.g., electrical signals representing sounds) and phonemes or other linguistic units of speech. An acoustic model may be created by using audio recordings of speech and corresponding transcriptions of the speech to train a predictive model (e.g., a statistical model) to identify the linguistic units represented by audio signals. Different acoustic models may be specifically trained for use by a particular user (or group of users), or for use in a particular environment. An acoustic model that has been specifically trained for a particular user or environment may provide more accurate results, when used by that user or within that environment, than general-purpose acoustic models or acoustic models trained for different users or environments, because different speakers can use different sounds to represent the same linguistic units, and different audio channels can produce different audio signals to represent the same speech. For example, an acoustic model trained using mobile phone conversations in the Canadian dialect of the English language may provide relatively high accuracy for mobile-phone based speech in the Canadian English dialect, whereas an acoustic model trained using toll quality audio of speech in the Australian dialect of the English language may provide relatively lower accuracy for mobile-phone based speech in the Canadian English dialect.
Language models generally use deterministic (e.g., grammar-based) techniques or stochastic (e.g., statistical) techniques to estimate which word or sequence of words is represented by a segment of speech. Statistical language models generally model the probability that a portion of speech represents a particular word or sequence of words. The probability that a particular portion of speech represents a particular word may depend on the speaker's preceding words, on the position of the word within a sentence, etc. For example, in American English, the word “touchdown” (which refers to a scoring play in American football) and the phrase “touched down” (which can refer to an event during the landing of an airplane) sound almost identical, but have very different meanings. A language model may distinguish between the word “touchdown” and the phrase “touched down” based on the speaker's preceding words, because the sequences of words “scored a touchdown” and “the plane touched down” are far more likely to occur than the sequence of words “scored a touched down” and “the plane touchdown.” A language model that has been specifically trained for speech relating to a particular topic may provide more accurate results, when used to model speech relating to that topic, than general-purpose language models or language models trained for different topics, because different words (or sequences of words) are more or less likely to occur in discussions of different topics. Topic-specific language models are commonly used for dictation applications relating to the practice of law or the practice of medicine, because the vocabularies and language patterns of lawyers and doctors can be highly specialized and therefore difficult to recognize using general-purpose language models.
Process monitoring and control relates to the viewing and control of parameters of one or more processes, environments in which the processes are performed, and/or the operation of equipment executing the processes. In the manufacturing context, for example, process parameters can include duration, temperature, pressure, speed, quantity, and/or direction of motion of a particular piece of machinery. In other processes (e.g., processes performed in the course of using and maintaining information systems, management systems, etc.), the parameters can include the temperature of the operating environment, throughput (transactional and/or packet-based), downtime, usage, etc. Automation, or process control, systems can be used to help manage production, monitor and maintain equipment, view performance and operational trends, and/or perform business functions such as remotely modifying operational parameters, visually inspecting equipment or operations, and scheduling maintenance.